This notebook is in continuation of another notebook. If you haven't gone through the previous notebook it will be fine as this is also a standalone notebook but if you are a beginner I do recommend going through the previous notebook first.

Urban 8k Audio Classification Project

Dataset Description

This dataset contains 8732 labeled sound excerpts (<=4s) of urban sounds from 10 classes: air_conditioner, car_horn, children_playing, dog_bark, drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. The classes are drawn from the urban sound taxonomy. For a detailed description of the dataset and how it was compiled please refer to our paper. All excerpts are taken from field recordings uploaded to www.freesound.org. The files are pre-sorted into ten folds (folders named fold1-fold10) to help in the reproduction of and comparison with the automatic classification results reported in the article above.

In addition to the sound excerpts, a CSV file containing metadata about each excerpt is also provided.

8732 audio files of urban sounds (see description above) in WAV format. The sampling rate, bit depth, and number of channels are the same as those of the original file uploaded to Freesound (and hence may vary from file to file).

Task Description

The Dataset contains 10 classes, we will do classification task on the audio file obtained.

A numeric identifier of the sound class:
0 = air_conditioner
1 = car_horn
2 = children_playing
3 = dog_bark
4 = drilling
5 = engine_idling
6 = gun_shot
7 = jackhammer
8 = siren
9 = street_music

How to get the Urban8K Dataset?

Just go to Urban8K Website and fill a simple form to download the dataset. Since the dataset is > 5GB in compressed form itself, it's better to copy the download link and directly !wget it to the colab and move to the drive for further use.

Code Block Below Downloads the Dataset and Unzips the tarball, this might take a while to download and unzip.

In [ ]:
!wget https://goo.gl/8hY5ER
!tar -xvf 8hY5ER -C ./

--2020-12-16 17:48:45--  https://goo.gl/8hY5ER
Resolving goo.gl (goo.gl)... 64.233.170.102, 64.233.170.113, 64.233.170.101, ...
Connecting to goo.gl (goo.gl)|64.233.170.102|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://zenodo.org/record/1203745/files/UrbanSound8K.tar.gz [following]
--2020-12-16 17:48:46--  https://zenodo.org/record/1203745/files/UrbanSound8K.tar.gz
Resolving zenodo.org (zenodo.org)... 137.138.76.77
Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 6023741708 (5.6G) [application/octet-stream]
Saving to: ‘8hY5ER’

8hY5ER              100%[===================>]   5.61G  7.08MB/s    in 10m 55s 

2020-12-16 17:59:42 (8.77 MB/s) - ‘8hY5ER’ saved [6023741708/6023741708]

tar: .: Not found in archive
tar: Exiting with failure status due to previous errors
  • Mount Google Drive, Create a Folder named Urban8KZip in it.
  • Copy the unzipped urban8kzipped contents to the Urban8KZip Directory
In [ ]:
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive
In [ ]:
!mkdir /content/drive/MyDrive/Urban8KZip
!cp ./urban8kzipped /content/drive/MyDrive/Urban8KZip

In case running notebook again,

If you do run notebook again there is no reason to download dataset again and move to drive, just mount the drive and copy the content from the Urban8KZip folder to the colab.

Run this next cell if running the notebook again, Don't run the above cell and downloading cells(in starting), just mount the colab and run the next cell.

In [ ]:
!tar -xvf /content/drive/MyDrive/Urban8KZip/urban8kzipped -C ./

Streaming output truncated to the last 5000 lines.
UrbanSound8K/audio/fold4/17480-2-0-6.wav
UrbanSound8K/audio/fold4/17480-2-0-9.wav
UrbanSound8K/audio/fold4/175904-2-0-11.wav
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UrbanSound8K/audio/fold9/85664-3-0-0.wav
UrbanSound8K/audio/fold9/87562-6-0-0.wav
UrbanSound8K/audio/fold9/94182-9-0-0.wav
UrbanSound8K/audio/fold9/94182-9-0-10.wav
UrbanSound8K/audio/fold9/94182-9-0-16.wav
UrbanSound8K/audio/fold9/94182-9-0-20.wav
UrbanSound8K/audio/fold9/94182-9-0-23.wav
UrbanSound8K/audio/fold9/94182-9-0-25.wav
UrbanSound8K/audio/fold9/94182-9-0-9.wav
UrbanSound8K/audio/fold9/96921-9-0-1.wav
UrbanSound8K/audio/fold9/96921-9-0-11.wav
UrbanSound8K/audio/fold9/96921-9-0-17.wav
UrbanSound8K/audio/fold9/96921-9-0-18.wav
UrbanSound8K/audio/fold9/96921-9-0-4.wav
UrbanSound8K/audio/fold9/96921-9-0-9.wav
UrbanSound8K/audio/fold9/98859-7-0-0.wav
UrbanSound8K/audio/fold9/98859-7-1-0.wav
UrbanSound8K/audio/fold9/98859-7-2-0.wav
UrbanSound8K/audio/fold9/98859-7-2-1.wav
UrbanSound8K/audio/fold9/98859-7-2-2.wav
UrbanSound8K/audio/fold9/98859-7-2-3.wav
UrbanSound8K/audio/fold9/98859-7-2-4.wav
UrbanSound8K/audio/fold9/98859-7-2-5.wav
UrbanSound8K/audio/fold9/99500-2-0-1.wav
UrbanSound8K/audio/fold9/99500-2-0-11.wav
UrbanSound8K/audio/fold9/99500-2-0-2.wav
UrbanSound8K/audio/fold9/99500-2-0-23.wav
UrbanSound8K/audio/fold9/99500-2-0-29.wav
UrbanSound8K/audio/fold9/99500-2-0-39.wav
UrbanSound8K/audio/fold9/99500-2-0-41.wav
UrbanSound8K/audio/fold9/99500-2-0-50.wav
UrbanSound8K/FREESOUNDCREDITS.txt
UrbanSound8K/metadata/
UrbanSound8K/metadata/.DS_Store
UrbanSound8K/metadata/UrbanSound8K.csv
UrbanSound8K/UrbanSound8K_README.txt

Data Analysis, Exploration and Visualizations

Some Information About Dataset.

This dataset has audio belonging to 10 classes, unlike images Audio data is different, it's similar in some senses but there are certain differences which are unique to audio data.

Before even using audio file dataset, the first challenge in itself is that audio files are continous data and our computers can only store discrete data, so in what way we digitize it such that we can easily store.

Apparently the solution is that we figure out a way that is discrete yet continous. Sounds misleading but what we do is we instead of storing continous value, we just sample it at fixed durations like 0.2 second apart or less or more. We just sample it and the rate at which it is sampled is known as Sampling rate.

my img
Image Courtesy Analytics Vidhya

When we say we sample it, what are we actually doing?

We are recording the amplitude at that point.

  • So the audio data is nothing just the amplitude stored at regular intervals which mimic the original continous wave.

  • We take and store thousands of measurements per second. If we can take tons of measurements extremely quickly with enough possible amplitude values, we can effectively use these snapshots to reconstruct the resolution and complexity of an analog wave.

my img
Image Courtesy izotope
In [ ]:
import IPython.display as ipd #Allows Audio files to be played directly in the notebook
import librosa #library we will to analyze sounds
import librosa.display #library module which helps visualize the waves
In [ ]:
import os
import glob
import numpy as np
import pandas as pd
import random

import matplotlib.pyplot as plt
import seaborn as sns

sns.set_style("whitegrid")

Reading audio files

In [ ]:
audios = glob.glob(os.path.join("/content/UrbanSound8K/audio/*/*.wav"), recursive=True)

print(f"Total Audio Files : {len(audios)}")

Total Audio Files : 8732
In [ ]:
audios[:5] #Contains path of all audios
Out[ ]:
['/content/UrbanSound8K/audio/fold1/78360-4-0-21.wav',
 '/content/UrbanSound8K/audio/fold1/106905-8-0-3.wav',
 '/content/UrbanSound8K/audio/fold1/176787-5-0-26.wav',
 '/content/UrbanSound8K/audio/fold1/103258-5-0-19.wav',
 '/content/UrbanSound8K/audio/fold1/43786-3-0-0.wav']

Hit the play icon below and listen to the sound, try to remember the sound and the plot generated do it several times and try to pick up pattern yourself.

To read audio files, we will be using,

data, sample_rate = librosa.load(audio_path)

librosa by default reads the audio file at sr(sampling rate) = 22050, if you want your custom sampling here you can pass sr = your_rate in the librosa.load function

In [ ]:
plt.figure(figsize=(12,4))

play_audio = random.choice(audios)
data, sample_rate = librosa.load(play_audio)
librosa.display.waveplot(data)
plt.title("Sound")
ipd.Audio(play_audio)
Out[ ]:

If you managed to pick up patterns, you can see it becomes a visual problem as well. Just a thought.

In [ ]:
plt.figure(figsize=(12,4))
play_audio = random.choice(audios)
data, sample_rate = librosa.load(play_audio, sr=100)
librosa.display.waveplot(data)
plt.title("Sound")
ipd.Audio(play_audio)
Out[ ]:

Plotting same audio at different sampling rate

Higher the sampling rate, higher the crisp of plot because less error while trying to recreate original audio with higher number of samples and vice versa.

In [ ]:
fig, axs = plt.subplots(2,2, figsize=(15,12))
fig.suptitle("Sampling of Same Audio File With Different values")
axs = np.reshape(axs,-1)
play_audio = random.choice(audios)
srs = [100, 2000, 22050, 44100]
for ax,sr in zip(axs,srs):
  try:
    data, sample_rate = librosa.load(play_audio, sr=sr)
    librosa.display.waveplot(data, sr=sample_rate, ax=ax)
    ax.set_title(f"Sampling Rate {sr} Hz")
  except:
    print("Run Again Some Unknown Error")

ipd.Audio(play_audio)
Out[ ]:

Reading Metadata for the dataset

In [ ]:
metadata = pd.read_csv("/content/UrbanSound8K/metadata/UrbanSound8K.csv")

metadata.head()
Out[ ]:
slice_file_name fsID start end salience fold classID class
0 100032-3-0-0.wav 100032 0.0 0.317551 1 5 3 dog_bark
1 100263-2-0-117.wav 100263 58.5 62.500000 1 5 2 children_playing
2 100263-2-0-121.wav 100263 60.5 64.500000 1 5 2 children_playing
3 100263-2-0-126.wav 100263 63.0 67.000000 1 5 2 children_playing
4 100263-2-0-137.wav 100263 68.5 72.500000 1 5 2 children_playing

Anatomy of Metadata csv

  • UrbanSound8K the main dataset directory contains a two folders audio and metadata, the audio is where all the audio files are located, if you are running this in colab on right if you click on the UrbanSound8K Folder you can see that it has the said two folders by clicking on it.

  • audio folder in itself contains 10 folders named fold1, fold2 ..... fold10. These folds contains audio files details of which are mentioned in above csv or dataframe.

  • If we join "UrbanSound8K/audio/" + "fold" + fold_value(1/2/...10) + slice_file_name it becomes path for the audio file as well.(This logic will be implemented later, keep it in mind till then.)

Selecting random row for each class in metadata

In [ ]:
unique_audios = metadata.groupby(['class']).apply(lambda sub_df : sub_df.sample()).reset_index(drop = True)
In [ ]:
unique_audios
Out[ ]:
slice_file_name fsID start end salience fold classID class
0 119067-0-0-1.wav 119067 0.500000 4.500000 1 10 0 air_conditioner
1 199769-1-0-9.wav 199769 5.348273 9.348273 1 3 1 car_horn
2 162318-2-0-39.wav 162318 19.500000 23.500000 1 5 2 children_playing
3 52740-3-0-0.wav 52740 68.097690 72.097690 2 9 3 dog_bark
4 180125-4-1-7.wav 180125 44.348486 48.348486 1 5 4 drilling
5 195451-5-0-18.wav 195451 11.249062 15.249062 1 3 5 engine_idling
6 46656-6-5-0.wav 46656 10.761675 12.112513 1 1 6 gun_shot
7 62837-7-1-28.wav 62837 275.706708 279.706708 1 3 7 jackhammer
8 24347-8-0-81.wav 24347 44.620371 48.620371 2 4 8 siren
9 42955-9-0-12.wav 42955 6.000000 10.000000 1 6 9 street_music

Plotting selected rows

In [ ]:
fig, axs = plt.subplots(5,2,figsize=(15,8),constrained_layout=True)
axs = np.reshape(axs, -1)

for (index,row),ax in zip(unique_audios.iterrows(),axs):
  ax.set_title(row.values[-1])
  data, sr = librosa.load(f"/content/UrbanSound8K/audio/fold{row.values[-3]}/" + row.values[0])
  _ = librosa.display.waveplot(data, sr=sample_rate, ax=ax)

MFCC Feature Extractor : Different from previous notebook's MFCC as it doesn't take mean, it directly returns the features.

In [ ]:
def extract_features(file_name, n_mfcc=40):
    try:
        audio, sample_rate = librosa.load(file_name, res_type='kaiser_fast') 
        mfccs = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=n_mfcc)
        
    except Exception as e:
        print("Error encountered while parsing file: ", file)
        return None 
    return mfccs

Reading audios again, so we don't have to scroll up again.

In [ ]:
audios = glob.glob(os.path.join("/content/UrbanSound8K/audio/*/*.wav"), recursive=True)

print(f"Total Audio Files : {len(audios)}")

Total Audio Files : 8732

Extracting Features and Shapes for further analysis.

In [ ]:
from collections import Counter
from tqdm.notebook import tqdm

extract_features_shape = []
extract_features_values = []

for audio_file in tqdm(audios):
  features = extract_features(audio_file)
  extract_features_values.append(features)
  extract_features_shape.append(features.shape)



















In [ ]:



    


unique_shapes = set(extract_features_shape)
list(unique_shapes)[:4]



    















    
Out[ ]:








[(40, 3), (40, 88), (40, 173), (40, 22)]





















Since the shape of features vary widely, CNN won't work directly, we have to pade them all to constant length i.e to max length.












In [ ]:



    


max_dim = sorted(list(unique_shapes), key=lambda shape : shape[1]) #Sorting unique shape list on basis of their second component(along the row)
max_len = max_dim[-1][1]



    















Standardizing Length of the Dataset














    

  • Our MFCC Features are 2D, we mentioned the number of mfcc in the previous function extract_features. We will have all mfcc in shape of (40, n) where n can be any positive integer. For m feature_arrays the dimensions look like (m,40,n). Where n varies for each array.
    

    • 

  • We will pad the sequences such that the final dimension of the feature_arrays become (m,40,max(n for n of all arrays). Let's say max_len is the maximum_length along the row for features. The final dimensions are (m,40,max_len) all padded by zero to match the max_len.
    

    • 














In [ ]:



    


def padded_sequence(feature_values, max_len = None):
  if max_len is None:
    print("Fix Max Length First")
    return None

  feature_len = feature_values.shape[1]
  pad_dim = ((0,0), (0, max_len - feature_len))
  return np.pad(feature_values, pad_dim)



    













In [ ]:



    


padded_features = list(map(lambda features : padded_sequence(features, max_len), extract_features_values))



    















    

  • Mapping audios to their respective MFCC features in form of a dict.
    • 














In [ ]:



    


audio_2_features = dict(zip(audios, padded_features))



    















Data Mapping












In [ ]:



    


metadata = pd.read_csv("/content/UrbanSound8K/metadata/UrbanSound8K.csv")

metadata.head()



    















    
Out[ ]:











  
    

      
      slice_file_name
      fsID
      start
      end
      salience
      fold
      classID
      class
    

  
  
    

      0
      100032-3-0-0.wav
      100032
      0.0
      0.317551
      1
      5
      3
      dog_bark
    

    

      1
      100263-2-0-117.wav
      100263
      58.5
      62.500000
      1
      5
      2
      children_playing
    

    

      2
      100263-2-0-121.wav
      100263
      60.5
      64.500000
      1
      5
      2
      children_playing
    

    

      3
      100263-2-0-126.wav
      100263
      63.0
      67.000000
      1
      5
      2
      children_playing
    

    

      4
      100263-2-0-137.wav
      100263
      68.5
      72.500000
      1
      5
      2
      children_playing
    

  
























    

  • Adding file_path column which equals to base_path + fold + slice_file_name.
    • 

  • file_path is the complete relative path that can be used to read files directly.
    • 














In [ ]:



    


base_path = "/content/UrbanSound8K/audio"
metadata['file_path'] = metadata.apply(lambda row : os.path.join(*[base_path, f"fold{row['fold']}", row['slice_file_name']]), axis=1)



    















    

  • Creating new column features which is file_path mapped to MFCC Features extracted before.
    • 














In [ ]:



    


metadata['features'] = metadata['file_path'].map(audio_2_features)

metadata.head()



    















    
Out[ ]:











  
    

      
      slice_file_name
      fsID
      start
      end
      salience
      fold
      classID
      class
      file_path
      features
    

  
  
    

      0
      100032-3-0-0.wav
      100032
      0.0
      0.317551
      1
      5
      3
      dog_bark
      /content/UrbanSound8K/audio/fold5/100032-3-0-0...
      [[-306.772569323401, -177.59208535704371, -99....
    

    

      1
      100263-2-0-117.wav
      100263
      58.5
      62.500000
      1
      5
      2
      children_playing
      /content/UrbanSound8K/audio/fold5/100263-2-0-1...
      [[-457.69536206989505, -451.0248088536722, -45...
    

    

      2
      100263-2-0-121.wav
      100263
      60.5
      64.500000
      1
      5
      2
      children_playing
      /content/UrbanSound8K/audio/fold5/100263-2-0-1...
      [[-468.03671021239467, -467.4226560550245, -48...
    

    

      3
      100263-2-0-126.wav
      100263
      63.0
      67.000000
      1
      5
      2
      children_playing
      /content/UrbanSound8K/audio/fold5/100263-2-0-1...
      [[-422.4221469201535, -411.9085176553704, -409...
    

    

      4
      100263-2-0-137.wav
      100263
      68.5
      72.500000
      1
      5
      2
      children_playing
      /content/UrbanSound8K/audio/fold5/100263-2-0-1...
      [[-438.1016202414386, -434.47786480748454, -44...
    

  
























    

  • Let's save this metadata, move to gdrive and load it back from there.
    • 














In [ ]:



    


metadata.to_json("metadata_features.json", orient='records')



    













In [ ]:



    


#to copy metadata from colab to gdrive.
!cp ./metadata_features.json /content/drive/MyDrive/Urban8KZip/



    















Once metadata is copied there is no need run all cells from above in case of running again or disconnection or anything else, just load the packages, mount the drive, and start running from the cells below.












In [ ]:



    


#To copy metadata to notebook from gdrive.
!cp  /content/drive/MyDrive/Urban8KZip/metadata_features.json ./



    













In [ ]:



    


metadata = pd.read_json("metadata_features.json", orient='records')



    













In [ ]:



    


metadata.head()



    















    
Out[ ]:











  
    

      
      slice_file_name
      fsID
      start
      end
      salience
      fold
      classID
      class
      file_path
      features
    

  
  
    

      0
      100032-3-0-0.wav
      100032
      0.0
      0.317551
      1
      5
      3
      dog_bark
      /content/UrbanSound8K/audio/fold5/100032-3-0-0...
      [[-306.7725693234, -177.592085357, -99.1361699...
    

    

      1
      100263-2-0-117.wav
      100263
      58.5
      62.500000
      1
      5
      2
      children_playing
      /content/UrbanSound8K/audio/fold5/100263-2-0-1...
      [[-457.6953620699, -451.0248088537, -450.68616...
    

    

      2
      100263-2-0-121.wav
      100263
      60.5
      64.500000
      1
      5
      2
      children_playing
      /content/UrbanSound8K/audio/fold5/100263-2-0-1...
      [[-468.0367102124, -467.422656055, -481.046543...
    

    

      3
      100263-2-0-126.wav
      100263
      63.0
      67.000000
      1
      5
      2
      children_playing
      /content/UrbanSound8K/audio/fold5/100263-2-0-1...
      [[-422.4221469202, -411.9085176554, -409.46240...
    

    

      4
      100263-2-0-137.wav
      100263
      68.5
      72.500000
      1
      5
      2
      children_playing
      /content/UrbanSound8K/audio/fold5/100263-2-0-1...
      [[-438.1016202414, -434.4778648075, -443.32839...
    

  
























    

  • Creating a featuredf dataframe to store features and their respective categories.
    • 














In [ ]:



    


featuresdf = pd.DataFrame()
featuresdf['feature'] = metadata['features']
featuresdf['class_label'] = metadata['class']

featuresdf['class_label'] = pd.Categorical(featuresdf['class_label'])
featuresdf['class_category'] = featuresdf['class_label'].cat.codes



    















    

  • Instead of categorizing the class column and getting the categorical codes back we could have used the class ids. Same thing!!
    • 














In [ ]:



    


featuresdf.head()



    















    
Out[ ]:











  
    

      
      feature
      class_label
      class_category
    

  
  
    

      0
      [[-306.7725693234, -177.592085357, -99.1361699...
      dog_bark
      3
    

    

      1
      [[-457.6953620699, -451.0248088537, -450.68616...
      children_playing
      2
    

    

      2
      [[-468.0367102124, -467.422656055, -481.046543...
      children_playing
      2
    

    

      3
      [[-422.4221469202, -411.9085176554, -409.46240...
      children_playing
      2
    

    

      4
      [[-438.1016202414, -434.4778648075, -443.32839...
      children_playing
      2
    

  
























Deep Learning Modelling














Train Test Split
    

  • X are mfcc features.
    • 

  • y are class categories.
    • 

  • Since all of the class don't have same instances let's stratify on the y.
    • 














In [ ]:



    


from sklearn.model_selection import train_test_split 
X = featuresdf.feature
y = featuresdf.class_category
x_train, x_test, y_train, y_test = train_test_split(list(X.values), list(y.values), test_size=0.2, random_state = 42, stratify=y)



    













In [ ]:



    


import torch
import torch.nn as nn
import torch.nn.functional as F

import torchvision

from torch.utils.data import TensorDataset,DataLoader



    













In [ ]:



    


x_train_tensor = torch.tensor(x_train, dtype=torch.float32)
y_train_tensor = torch.tensor(y_train, dtype=torch.long)

x_test_tensor = torch.tensor(x_test, dtype=torch.float32)
y_test_tensor = torch.tensor(y_test, dtype=torch.long)

print(x_train_tensor.dtype, y_train_tensor.dtype)
print(x_test_tensor.dtype, y_test_tensor.dtype)



    















    






torch.float32 torch.int64
torch.float32 torch.int64



















In [ ]:



    


print(x_train_tensor.shape, x_test_tensor.shape)



    















    






torch.Size([6985, 40, 174]) torch.Size([1747, 40, 174])





















Fixing the shapes of features for Pytorch CNN.
    

  • CNN layers expect the output to be of dimensions [batch_size, num_channels, len_y, len_x].
    • 

  • Our x_train_tensor has shape [6885, 40, 174] it is lacking num_channels dimension,  since the features were 2D we will say that num_channels = 1.
    • 

  • we will unsqueeze the tensor along the dim=1(where num_channels should be) adding a new dimension as num_channels.
    • 














In [ ]:



    


x_train_tensor = x_train_tensor.unsqueeze(1)
x_test_tensor = x_test_tensor.unsqueeze(1)

print(x_train_tensor.shape, x_test_tensor.shape)



    















    






torch.Size([6985, 1, 40, 174]) torch.Size([1747, 1, 40, 174])





















Creating Tensor Dataset












In [ ]:



    


train_dataset = TensorDataset(x_train_tensor,y_train_tensor)
test_dataset = TensorDataset(x_test_tensor,y_test_tensor)



    















Creating Data Loaders












In [ ]:



    


train_dl = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_dl = DataLoader(test_dataset, batch_size=64*2)



    















    

  • Setting the device to cuda or cpu depending upon the availiablity.
    • 














In [ ]:



    


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device



    















    
Out[ ]:








device(type='cuda')





















Loading created models from github gist.












In [ ]:



    


!git clone https://gist.github.com/ce6fa5ed83c78ce95244fd938b93655a.git
!mv ce6fa5ed83c78ce95244fd938b93655a/* ./
!rm -rf ce6fa5ed83c78ce95244fd938b93655a



    















    






Cloning into 'ce6fa5ed83c78ce95244fd938b93655a'...
remote: Enumerating objects: 31, done.
remote: Counting objects: 100% (31/31), done.
remote: Compressing objects: 100% (31/31), done.
remote: Total 31 (delta 13), reused 0 (delta 0), pack-reused 0
Unpacking objects: 100% (31/31), done.



















In [ ]:



    


from urban_base_model_cnn import UrbanSoundBase

from model_cnn_6_layers_simple import CNNModel_6Layers
from model_cnn_8_layers_simple import CNNModel_8Layers
from model_cnn_4_layers_simple import CNNModel_4Layers
from model_cnn_dynamic import DynamicModel



    















Dynamic Deep Learning Model












In [ ]:



    


input = torch.randn(32,1,40,174)
input_channels = 1
cnn_hidden_channels = [16, 32, 32, 64]
hidden_sizes = [128]
output_size = 10
sample_image_batch_dim = (32,1,40,174)
model = DynamicModel(input_channels, cnn_hidden_channels, hidden_sizes, output_size, sample_image_batch_dim)
model = model.to(device)



    















Sample Model Architecture : Customizable Model












In [ ]:



    


model



    















    
Out[ ]:








DynamicModel(
  (layers): ModuleList(
    (0): Sequential(
      (conv_layer): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1))
      (max_pool_layer): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (relu_layer): ReLU()
    )
    (1): Sequential(
      (conv_layer): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))
      (relu_layer): ReLU()
    )
    (2): Sequential(
      (conv_layer): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1))
      (max_pool_layer): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (relu_layer): ReLU()
    )
    (3): Sequential(
      (conv_layer): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))
      (relu_layer): ReLU()
    )
    (4): Flatten(start_dim=1, end_dim=-1)
    (5): Sequential(
      (linear_layer): Linear(in_features=12480, out_features=128, bias=True)
      (relu_layer): ReLU()
    )
    (6): Sequential(
      (linear_layer): Linear(in_features=128, out_features=10, bias=True)
    )
  )
)





















Model Accuracy and Loss before Training












In [ ]:



    


model.eval()
valid_history = model.evaluate_validation(test_dl, device)
acc = torch.stack([valid['val_acc'] for valid in valid_history]).mean().item()
loss = torch.stack([valid['val_loss'] for valid in valid_history]).mean().item()

print(f"Accuracy : {acc:.3f}, Loss : {loss:.3f}")



    















    






Accuracy : 0.156, Loss : 2.297



















In [ ]:



    


def fit(model,train_dataloader, valid_dataloader, n_epochs,lr = 0.001, optim = torch.optim.Adam, device = device):
  model_history = {'train_loss' : [], 'valid_loss' : [], 'train_acc' : [], 'valid_acc' : []}
  optimizer = optim(model.parameters(), lr)

  for epoch in range(n_epochs):
    model.train()
    train_loss = []
    train_accuracy = []

    for image_label_batch in train_dataloader:
      image, label = image_label_batch[0].to(device), image_label_batch[1].to(device)
      loss_acc = model.training_step((image, label))
      loss = loss_acc['loss']
      acc = loss_acc['acc']
      loss.backward()
      train_loss.append(loss)
      train_accuracy.append(acc)

      optimizer.step()
      optimizer.zero_grad()
    
    model.eval()
    
    with torch.no_grad():
        val_history = model.evaluate_validation(valid_dataloader, device)
        result = model.epoch_end(val_history)

    model_history['train_loss'].append(torch.stack(train_loss).mean().item())
    model_history['valid_loss'].append(result['val_loss'])
    model_history['train_acc'].append(torch.stack(train_accuracy).mean().item())
    model_history['valid_acc'].append(result['val_acc'])
    print(f"Epoch : {epoch}, Train Loss : {model_history['train_loss'][-1]:.2f}, Train Accuracy : {model_history['train_acc'][-1]:.2f}, Validation Loss : {result['val_loss']:.2f}, Validation Accuracy : {result['val_acc']:.2f}")


  return model_history



    















Training, Validating Pre-Designed CNN Models














4 Layer CNN Model from model_cnn_4_layers_simple.py












In [ ]:



    


sample_image_batch_dim = (32,1,40,174)
sample_row_size,sample_col_size = 40,174

model = CNNModel_4Layers(10, sample_row_size, sample_col_size)
model = model.to(device)

history_CNNModel_4Layers = fit(model, train_dl, test_dl, 50, 0.001, optim=torch.optim.SGD)



    















    






Epoch : 0, Train Loss : 2.28, Train Accuracy : 0.46, Validation Loss : 1.46, Validation Accuracy : 0.52
Epoch : 1, Train Loss : 1.05, Train Accuracy : 0.65, Validation Loss : 1.22, Validation Accuracy : 0.58
Epoch : 2, Train Loss : 0.86, Train Accuracy : 0.71, Validation Loss : 1.40, Validation Accuracy : 0.54
Epoch : 3, Train Loss : 0.74, Train Accuracy : 0.76, Validation Loss : 0.92, Validation Accuracy : 0.69
Epoch : 4, Train Loss : 0.64, Train Accuracy : 0.80, Validation Loss : 1.44, Validation Accuracy : 0.57
Epoch : 5, Train Loss : 0.59, Train Accuracy : 0.82, Validation Loss : 0.95, Validation Accuracy : 0.67
Epoch : 6, Train Loss : 0.49, Train Accuracy : 0.85, Validation Loss : 0.84, Validation Accuracy : 0.73
Epoch : 7, Train Loss : 0.44, Train Accuracy : 0.87, Validation Loss : 1.05, Validation Accuracy : 0.69
Epoch : 8, Train Loss : 0.39, Train Accuracy : 0.88, Validation Loss : 0.62, Validation Accuracy : 0.80
Epoch : 9, Train Loss : 0.34, Train Accuracy : 0.90, Validation Loss : 0.83, Validation Accuracy : 0.72
Epoch : 10, Train Loss : 0.31, Train Accuracy : 0.91, Validation Loss : 0.76, Validation Accuracy : 0.77
Epoch : 11, Train Loss : 0.27, Train Accuracy : 0.92, Validation Loss : 0.59, Validation Accuracy : 0.81
Epoch : 12, Train Loss : 0.24, Train Accuracy : 0.94, Validation Loss : 0.94, Validation Accuracy : 0.75
Epoch : 13, Train Loss : 0.22, Train Accuracy : 0.94, Validation Loss : 1.21, Validation Accuracy : 0.68
Epoch : 14, Train Loss : 0.20, Train Accuracy : 0.95, Validation Loss : 0.51, Validation Accuracy : 0.84
Epoch : 15, Train Loss : 0.17, Train Accuracy : 0.96, Validation Loss : 1.04, Validation Accuracy : 0.71
Epoch : 16, Train Loss : 0.15, Train Accuracy : 0.96, Validation Loss : 0.55, Validation Accuracy : 0.83
Epoch : 17, Train Loss : 0.13, Train Accuracy : 0.97, Validation Loss : 0.51, Validation Accuracy : 0.83
Epoch : 18, Train Loss : 0.12, Train Accuracy : 0.98, Validation Loss : 1.18, Validation Accuracy : 0.71
Epoch : 19, Train Loss : 0.11, Train Accuracy : 0.98, Validation Loss : 0.52, Validation Accuracy : 0.85
Epoch : 20, Train Loss : 0.09, Train Accuracy : 0.98, Validation Loss : 0.45, Validation Accuracy : 0.86
Epoch : 21, Train Loss : 0.08, Train Accuracy : 0.99, Validation Loss : 0.55, Validation Accuracy : 0.85
Epoch : 22, Train Loss : 0.07, Train Accuracy : 0.99, Validation Loss : 0.44, Validation Accuracy : 0.87
Epoch : 23, Train Loss : 0.06, Train Accuracy : 0.99, Validation Loss : 0.49, Validation Accuracy : 0.85
Epoch : 24, Train Loss : 0.06, Train Accuracy : 0.99, Validation Loss : 0.56, Validation Accuracy : 0.83
Epoch : 25, Train Loss : 0.05, Train Accuracy : 0.99, Validation Loss : 0.45, Validation Accuracy : 0.86
Epoch : 26, Train Loss : 0.05, Train Accuracy : 1.00, Validation Loss : 0.58, Validation Accuracy : 0.82
Epoch : 27, Train Loss : 0.05, Train Accuracy : 1.00, Validation Loss : 0.45, Validation Accuracy : 0.87
Epoch : 28, Train Loss : 0.04, Train Accuracy : 1.00, Validation Loss : 0.73, Validation Accuracy : 0.81
Epoch : 29, Train Loss : 0.04, Train Accuracy : 1.00, Validation Loss : 0.45, Validation Accuracy : 0.87
Epoch : 30, Train Loss : 0.03, Train Accuracy : 1.00, Validation Loss : 0.53, Validation Accuracy : 0.84
Epoch : 31, Train Loss : 0.03, Train Accuracy : 1.00, Validation Loss : 0.46, Validation Accuracy : 0.87
Epoch : 32, Train Loss : 0.03, Train Accuracy : 1.00, Validation Loss : 0.47, Validation Accuracy : 0.86
Epoch : 33, Train Loss : 0.02, Train Accuracy : 1.00, Validation Loss : 0.49, Validation Accuracy : 0.86
Epoch : 34, Train Loss : 0.02, Train Accuracy : 1.00, Validation Loss : 0.45, Validation Accuracy : 0.87
Epoch : 35, Train Loss : 0.02, Train Accuracy : 1.00, Validation Loss : 0.53, Validation Accuracy : 0.86
Epoch : 36, Train Loss : 0.02, Train Accuracy : 1.00, Validation Loss : 0.48, Validation Accuracy : 0.87
Epoch : 37, Train Loss : 0.02, Train Accuracy : 1.00, Validation Loss : 0.46, Validation Accuracy : 0.87
Epoch : 38, Train Loss : 0.02, Train Accuracy : 1.00, Validation Loss : 0.48, Validation Accuracy : 0.87
Epoch : 39, Train Loss : 0.02, Train Accuracy : 1.00, Validation Loss : 0.47, Validation Accuracy : 0.87
Epoch : 40, Train Loss : 0.02, Train Accuracy : 1.00, Validation Loss : 0.46, Validation Accuracy : 0.88
Epoch : 41, Train Loss : 0.02, Train Accuracy : 1.00, Validation Loss : 0.55, Validation Accuracy : 0.85
Epoch : 42, Train Loss : 0.01, Train Accuracy : 1.00, Validation Loss : 0.48, Validation Accuracy : 0.87
Epoch : 43, Train Loss : 0.01, Train Accuracy : 1.00, Validation Loss : 0.47, Validation Accuracy : 0.87
Epoch : 44, Train Loss : 0.02, Train Accuracy : 1.00, Validation Loss : 0.47, Validation Accuracy : 0.87
Epoch : 45, Train Loss : 0.01, Train Accuracy : 1.00, Validation Loss : 0.47, Validation Accuracy : 0.87
Epoch : 46, Train Loss : 0.01, Train Accuracy : 1.00, Validation Loss : 0.48, Validation Accuracy : 0.88
Epoch : 47, Train Loss : 0.01, Train Accuracy : 1.00, Validation Loss : 0.48, Validation Accuracy : 0.88
Epoch : 48, Train Loss : 0.01, Train Accuracy : 1.00, Validation Loss : 0.48, Validation Accuracy : 0.88
Epoch : 49, Train Loss : 0.01, Train Accuracy : 1.00, Validation Loss : 0.48, Validation Accuracy : 0.88



















In [ ]:



    


fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,6))

fig.suptitle(f"model_nn_4_layers_simple : 1000 Epochs, 0.001 SGD", fontsize=16)
ax1.plot(history_CNNModel_4Layers['train_acc'], label='train accuracy')
ax1.plot(history_CNNModel_4Layers['valid_acc'], label='valid accuracy')
ax1.legend()


ax2.plot(history_CNNModel_4Layers['train_loss'], label='train loss')
ax2.plot(history_CNNModel_4Layers['valid_loss'], label='valid loss')
ax2.legend()

fig.show()



    















    




























6 Layer CNN Model from model_cnn_6_layers_simple.py












In [ ]:



    


sample_image_batch_dim = (32,1,40,174)
sample_row_size,sample_col_size = 40,174

model = CNNModel_6Layers(10, sample_row_size, sample_col_size)
model = model.to(device)

history_CNNModel_6Layers = fit(model, train_dl, test_dl, 50, 0.001, optim=torch.optim.SGD)



    















    






Epoch : 0, Train Loss : 2.09, Train Accuracy : 0.21, Validation Loss : 1.94, Validation Accuracy : 0.27
Epoch : 1, Train Loss : 1.77, Train Accuracy : 0.36, Validation Loss : 1.72, Validation Accuracy : 0.36
Epoch : 2, Train Loss : 1.56, Train Accuracy : 0.45, Validation Loss : 1.67, Validation Accuracy : 0.41
Epoch : 3, Train Loss : 1.44, Train Accuracy : 0.50, Validation Loss : 1.48, Validation Accuracy : 0.47
Epoch : 4, Train Loss : 1.33, Train Accuracy : 0.54, Validation Loss : 1.37, Validation Accuracy : 0.54
Epoch : 5, Train Loss : 1.26, Train Accuracy : 0.57, Validation Loss : 1.39, Validation Accuracy : 0.53
Epoch : 6, Train Loss : 1.22, Train Accuracy : 0.59, Validation Loss : 1.45, Validation Accuracy : 0.49
Epoch : 7, Train Loss : 1.17, Train Accuracy : 0.60, Validation Loss : 1.28, Validation Accuracy : 0.56
Epoch : 8, Train Loss : 1.12, Train Accuracy : 0.62, Validation Loss : 1.18, Validation Accuracy : 0.60
Epoch : 9, Train Loss : 1.07, Train Accuracy : 0.63, Validation Loss : 1.52, Validation Accuracy : 0.50
Epoch : 10, Train Loss : 1.04, Train Accuracy : 0.65, Validation Loss : 1.14, Validation Accuracy : 0.63
Epoch : 11, Train Loss : 0.99, Train Accuracy : 0.66, Validation Loss : 1.06, Validation Accuracy : 0.65
Epoch : 12, Train Loss : 0.97, Train Accuracy : 0.67, Validation Loss : 2.01, Validation Accuracy : 0.40
Epoch : 13, Train Loss : 0.94, Train Accuracy : 0.68, Validation Loss : 1.20, Validation Accuracy : 0.60
Epoch : 14, Train Loss : 0.91, Train Accuracy : 0.69, Validation Loss : 1.00, Validation Accuracy : 0.65
Epoch : 15, Train Loss : 0.87, Train Accuracy : 0.70, Validation Loss : 0.97, Validation Accuracy : 0.67
Epoch : 16, Train Loss : 0.83, Train Accuracy : 0.73, Validation Loss : 1.02, Validation Accuracy : 0.66
Epoch : 17, Train Loss : 0.80, Train Accuracy : 0.73, Validation Loss : 0.89, Validation Accuracy : 0.70
Epoch : 18, Train Loss : 0.77, Train Accuracy : 0.74, Validation Loss : 0.88, Validation Accuracy : 0.71
Epoch : 19, Train Loss : 0.74, Train Accuracy : 0.75, Validation Loss : 0.84, Validation Accuracy : 0.72
Epoch : 20, Train Loss : 0.72, Train Accuracy : 0.76, Validation Loss : 0.95, Validation Accuracy : 0.68
Epoch : 21, Train Loss : 0.69, Train Accuracy : 0.77, Validation Loss : 0.94, Validation Accuracy : 0.68
Epoch : 22, Train Loss : 0.67, Train Accuracy : 0.78, Validation Loss : 0.74, Validation Accuracy : 0.75
Epoch : 23, Train Loss : 0.66, Train Accuracy : 0.78, Validation Loss : 0.94, Validation Accuracy : 0.66
Epoch : 24, Train Loss : 0.63, Train Accuracy : 0.79, Validation Loss : 0.73, Validation Accuracy : 0.76
Epoch : 25, Train Loss : 0.60, Train Accuracy : 0.80, Validation Loss : 0.70, Validation Accuracy : 0.77
Epoch : 26, Train Loss : 0.58, Train Accuracy : 0.81, Validation Loss : 0.83, Validation Accuracy : 0.74
Epoch : 27, Train Loss : 0.56, Train Accuracy : 0.82, Validation Loss : 0.69, Validation Accuracy : 0.77
Epoch : 28, Train Loss : 0.54, Train Accuracy : 0.82, Validation Loss : 0.76, Validation Accuracy : 0.74
Epoch : 29, Train Loss : 0.52, Train Accuracy : 0.83, Validation Loss : 1.00, Validation Accuracy : 0.68
Epoch : 30, Train Loss : 0.50, Train Accuracy : 0.84, Validation Loss : 0.66, Validation Accuracy : 0.78
Epoch : 31, Train Loss : 0.48, Train Accuracy : 0.84, Validation Loss : 0.70, Validation Accuracy : 0.77
Epoch : 32, Train Loss : 0.46, Train Accuracy : 0.85, Validation Loss : 0.98, Validation Accuracy : 0.68
Epoch : 33, Train Loss : 0.45, Train Accuracy : 0.86, Validation Loss : 0.62, Validation Accuracy : 0.80
Epoch : 34, Train Loss : 0.42, Train Accuracy : 0.86, Validation Loss : 0.69, Validation Accuracy : 0.76
Epoch : 35, Train Loss : 0.40, Train Accuracy : 0.87, Validation Loss : 0.62, Validation Accuracy : 0.80
Epoch : 36, Train Loss : 0.40, Train Accuracy : 0.87, Validation Loss : 0.92, Validation Accuracy : 0.68
Epoch : 37, Train Loss : 0.39, Train Accuracy : 0.87, Validation Loss : 0.59, Validation Accuracy : 0.81
Epoch : 38, Train Loss : 0.36, Train Accuracy : 0.89, Validation Loss : 0.94, Validation Accuracy : 0.71
Epoch : 39, Train Loss : 0.36, Train Accuracy : 0.88, Validation Loss : 0.59, Validation Accuracy : 0.80
Epoch : 40, Train Loss : 0.33, Train Accuracy : 0.89, Validation Loss : 0.56, Validation Accuracy : 0.81
Epoch : 41, Train Loss : 0.31, Train Accuracy : 0.90, Validation Loss : 1.01, Validation Accuracy : 0.69
Epoch : 42, Train Loss : 0.31, Train Accuracy : 0.90, Validation Loss : 0.59, Validation Accuracy : 0.82
Epoch : 43, Train Loss : 0.30, Train Accuracy : 0.90, Validation Loss : 0.89, Validation Accuracy : 0.75
Epoch : 44, Train Loss : 0.28, Train Accuracy : 0.91, Validation Loss : 0.56, Validation Accuracy : 0.82
Epoch : 45, Train Loss : 0.26, Train Accuracy : 0.92, Validation Loss : 0.61, Validation Accuracy : 0.80
Epoch : 46, Train Loss : 0.25, Train Accuracy : 0.92, Validation Loss : 0.76, Validation Accuracy : 0.78
Epoch : 47, Train Loss : 0.24, Train Accuracy : 0.92, Validation Loss : 0.52, Validation Accuracy : 0.83
Epoch : 48, Train Loss : 0.23, Train Accuracy : 0.93, Validation Loss : 0.64, Validation Accuracy : 0.79
Epoch : 49, Train Loss : 0.22, Train Accuracy : 0.93, Validation Loss : 0.50, Validation Accuracy : 0.84



















In [ ]:



    


fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,6))

fig.suptitle(f"model_cnn_6_layers_simple : 50 Epochs, 0.001 SGD", fontsize=16)
ax1.plot(history_CNNModel_6Layers['train_acc'], label='train accuracy')
ax1.plot(history_CNNModel_6Layers['valid_acc'], label='valid accuracy')
ax1.legend()


ax2.plot(history_CNNModel_6Layers['train_loss'], label='train loss')
ax2.plot(history_CNNModel_6Layers['valid_loss'], label='valid loss')
ax2.legend()

fig.show()



    















    




























8 Layer CNN Model from model_cnn_8_layers_simple.py












In [ ]:



    


sample_image_batch_dim = (32,1,40,174)
sample_row_size,sample_col_size = 40,174

model = CNNModel_8Layers(10, sample_row_size, sample_col_size)
model = model.to(device)

history_CNNModel_8Layers = fit(model, train_dl, test_dl, 50, 0.001, optim=torch.optim.SGD)



    















    






Epoch : 0, Train Loss : 2.29, Train Accuracy : 0.12, Validation Loss : 2.28, Validation Accuracy : 0.08
Epoch : 1, Train Loss : 2.26, Train Accuracy : 0.13, Validation Loss : 2.26, Validation Accuracy : 0.15
Epoch : 2, Train Loss : 2.24, Train Accuracy : 0.13, Validation Loss : 2.23, Validation Accuracy : 0.16
Epoch : 3, Train Loss : 2.22, Train Accuracy : 0.13, Validation Loss : 2.21, Validation Accuracy : 0.17
Epoch : 4, Train Loss : 2.20, Train Accuracy : 0.13, Validation Loss : 2.20, Validation Accuracy : 0.17
Epoch : 5, Train Loss : 2.19, Train Accuracy : 0.14, Validation Loss : 2.19, Validation Accuracy : 0.21
Epoch : 6, Train Loss : 2.18, Train Accuracy : 0.15, Validation Loss : 2.17, Validation Accuracy : 0.21
Epoch : 7, Train Loss : 2.17, Train Accuracy : 0.16, Validation Loss : 2.15, Validation Accuracy : 0.20
Epoch : 8, Train Loss : 2.15, Train Accuracy : 0.18, Validation Loss : 2.13, Validation Accuracy : 0.26
Epoch : 9, Train Loss : 2.11, Train Accuracy : 0.20, Validation Loss : 2.08, Validation Accuracy : 0.22
Epoch : 10, Train Loss : 2.07, Train Accuracy : 0.22, Validation Loss : 2.01, Validation Accuracy : 0.26
Epoch : 11, Train Loss : 2.00, Train Accuracy : 0.22, Validation Loss : 1.95, Validation Accuracy : 0.25
Epoch : 12, Train Loss : 1.95, Train Accuracy : 0.23, Validation Loss : 1.93, Validation Accuracy : 0.24
Epoch : 13, Train Loss : 1.92, Train Accuracy : 0.24, Validation Loss : 1.93, Validation Accuracy : 0.24
Epoch : 14, Train Loss : 1.90, Train Accuracy : 0.25, Validation Loss : 1.87, Validation Accuracy : 0.26
Epoch : 15, Train Loss : 1.85, Train Accuracy : 0.28, Validation Loss : 1.82, Validation Accuracy : 0.32
Epoch : 16, Train Loss : 1.82, Train Accuracy : 0.30, Validation Loss : 1.79, Validation Accuracy : 0.33
Epoch : 17, Train Loss : 1.78, Train Accuracy : 0.33, Validation Loss : 1.85, Validation Accuracy : 0.30
Epoch : 18, Train Loss : 1.74, Train Accuracy : 0.35, Validation Loss : 1.78, Validation Accuracy : 0.33
Epoch : 19, Train Loss : 1.71, Train Accuracy : 0.36, Validation Loss : 1.70, Validation Accuracy : 0.37
Epoch : 20, Train Loss : 1.70, Train Accuracy : 0.37, Validation Loss : 1.70, Validation Accuracy : 0.39
Epoch : 21, Train Loss : 1.67, Train Accuracy : 0.38, Validation Loss : 1.75, Validation Accuracy : 0.34
Epoch : 22, Train Loss : 1.65, Train Accuracy : 0.39, Validation Loss : 1.67, Validation Accuracy : 0.40
Epoch : 23, Train Loss : 1.63, Train Accuracy : 0.40, Validation Loss : 1.64, Validation Accuracy : 0.42
Epoch : 24, Train Loss : 1.61, Train Accuracy : 0.41, Validation Loss : 1.62, Validation Accuracy : 0.43
Epoch : 25, Train Loss : 1.59, Train Accuracy : 0.43, Validation Loss : 1.62, Validation Accuracy : 0.46
Epoch : 26, Train Loss : 1.57, Train Accuracy : 0.44, Validation Loss : 1.70, Validation Accuracy : 0.38
Epoch : 27, Train Loss : 1.56, Train Accuracy : 0.44, Validation Loss : 1.58, Validation Accuracy : 0.47
Epoch : 28, Train Loss : 1.55, Train Accuracy : 0.45, Validation Loss : 1.56, Validation Accuracy : 0.48
Epoch : 29, Train Loss : 1.52, Train Accuracy : 0.46, Validation Loss : 1.55, Validation Accuracy : 0.49
Epoch : 30, Train Loss : 1.50, Train Accuracy : 0.47, Validation Loss : 1.50, Validation Accuracy : 0.48
Epoch : 31, Train Loss : 1.48, Train Accuracy : 0.47, Validation Loss : 1.50, Validation Accuracy : 0.49
Epoch : 32, Train Loss : 1.47, Train Accuracy : 0.49, Validation Loss : 1.55, Validation Accuracy : 0.47
Epoch : 33, Train Loss : 1.45, Train Accuracy : 0.49, Validation Loss : 1.48, Validation Accuracy : 0.48
Epoch : 34, Train Loss : 1.43, Train Accuracy : 0.50, Validation Loss : 1.44, Validation Accuracy : 0.51
Epoch : 35, Train Loss : 1.42, Train Accuracy : 0.51, Validation Loss : 1.42, Validation Accuracy : 0.52
Epoch : 36, Train Loss : 1.41, Train Accuracy : 0.51, Validation Loss : 1.40, Validation Accuracy : 0.52
Epoch : 37, Train Loss : 1.37, Train Accuracy : 0.53, Validation Loss : 1.37, Validation Accuracy : 0.53
Epoch : 38, Train Loss : 1.37, Train Accuracy : 0.52, Validation Loss : 1.41, Validation Accuracy : 0.52
Epoch : 39, Train Loss : 1.35, Train Accuracy : 0.53, Validation Loss : 1.38, Validation Accuracy : 0.54
Epoch : 40, Train Loss : 1.34, Train Accuracy : 0.53, Validation Loss : 1.39, Validation Accuracy : 0.52
Epoch : 41, Train Loss : 1.33, Train Accuracy : 0.54, Validation Loss : 1.34, Validation Accuracy : 0.55
Epoch : 42, Train Loss : 1.33, Train Accuracy : 0.54, Validation Loss : 1.33, Validation Accuracy : 0.54
Epoch : 43, Train Loss : 1.30, Train Accuracy : 0.55, Validation Loss : 1.37, Validation Accuracy : 0.52
Epoch : 44, Train Loss : 1.29, Train Accuracy : 0.56, Validation Loss : 1.29, Validation Accuracy : 0.56
Epoch : 45, Train Loss : 1.28, Train Accuracy : 0.56, Validation Loss : 1.28, Validation Accuracy : 0.57
Epoch : 46, Train Loss : 1.27, Train Accuracy : 0.56, Validation Loss : 1.29, Validation Accuracy : 0.56
Epoch : 47, Train Loss : 1.26, Train Accuracy : 0.56, Validation Loss : 1.27, Validation Accuracy : 0.56
Epoch : 48, Train Loss : 1.24, Train Accuracy : 0.57, Validation Loss : 1.25, Validation Accuracy : 0.57
Epoch : 49, Train Loss : 1.23, Train Accuracy : 0.57, Validation Loss : 1.24, Validation Accuracy : 0.58



















In [ ]:



    


fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,6))

fig.suptitle(f"model_nn_8_layers_simple : 50 Epochs, 0.001 SGD", fontsize=16)
ax1.plot(history_CNNModel_8Layers['train_acc'], label='train accuracy')
ax1.plot(history_CNNModel_8Layers['valid_acc'], label='valid accuracy')
ax1.legend()


ax2.plot(history_CNNModel_8Layers['train_loss'], label='train loss')
ax2.plot(history_CNNModel_8Layers['valid_loss'], label='valid loss')
ax2.legend()

fig.show()



    















    




























Training, Validating Pre Designed Models












In [ ]:



    


input = torch.randn(32,1,40,174)
input_channels = 1
cnn_hidden_channels = [16, 32, 32, 64]
hidden_sizes = [128]
output_size = 10
sample_image_batch_dim = (32,1,40,174)
model = DynamicModel(input_channels, cnn_hidden_channels, hidden_sizes, output_size, sample_image_batch_dim)
model = model.to(device)

history_dynamic_6_layers = fit(model, train_dl, test_dl, 50, 0.001, optim=torch.optim.SGD)



    















    






Epoch : 0, Train Loss : 2.04, Train Accuracy : 0.25, Validation Loss : 2.03, Validation Accuracy : 0.24
Epoch : 1, Train Loss : 1.69, Train Accuracy : 0.39, Validation Loss : 1.85, Validation Accuracy : 0.31
Epoch : 2, Train Loss : 1.50, Train Accuracy : 0.46, Validation Loss : 1.92, Validation Accuracy : 0.29
Epoch : 3, Train Loss : 1.40, Train Accuracy : 0.52, Validation Loss : 1.51, Validation Accuracy : 0.45
Epoch : 4, Train Loss : 1.29, Train Accuracy : 0.56, Validation Loss : 1.52, Validation Accuracy : 0.48
Epoch : 5, Train Loss : 1.22, Train Accuracy : 0.59, Validation Loss : 1.41, Validation Accuracy : 0.51
Epoch : 6, Train Loss : 1.17, Train Accuracy : 0.60, Validation Loss : 1.34, Validation Accuracy : 0.54
Epoch : 7, Train Loss : 1.09, Train Accuracy : 0.63, Validation Loss : 1.64, Validation Accuracy : 0.46
Epoch : 8, Train Loss : 1.05, Train Accuracy : 0.65, Validation Loss : 1.11, Validation Accuracy : 0.61
Epoch : 9, Train Loss : 1.00, Train Accuracy : 0.66, Validation Loss : 1.27, Validation Accuracy : 0.55
Epoch : 10, Train Loss : 0.95, Train Accuracy : 0.68, Validation Loss : 1.29, Validation Accuracy : 0.55
Epoch : 11, Train Loss : 0.91, Train Accuracy : 0.70, Validation Loss : 1.45, Validation Accuracy : 0.49
Epoch : 12, Train Loss : 0.86, Train Accuracy : 0.71, Validation Loss : 1.09, Validation Accuracy : 0.61
Epoch : 13, Train Loss : 0.83, Train Accuracy : 0.72, Validation Loss : 1.00, Validation Accuracy : 0.66
Epoch : 14, Train Loss : 0.80, Train Accuracy : 0.74, Validation Loss : 1.21, Validation Accuracy : 0.60
Epoch : 15, Train Loss : 0.77, Train Accuracy : 0.75, Validation Loss : 0.95, Validation Accuracy : 0.68
Epoch : 16, Train Loss : 0.74, Train Accuracy : 0.75, Validation Loss : 1.08, Validation Accuracy : 0.63
Epoch : 17, Train Loss : 0.71, Train Accuracy : 0.77, Validation Loss : 1.01, Validation Accuracy : 0.66
Epoch : 18, Train Loss : 0.68, Train Accuracy : 0.77, Validation Loss : 1.77, Validation Accuracy : 0.54
Epoch : 19, Train Loss : 0.66, Train Accuracy : 0.78, Validation Loss : 1.23, Validation Accuracy : 0.61
Epoch : 20, Train Loss : 0.63, Train Accuracy : 0.79, Validation Loss : 1.21, Validation Accuracy : 0.63
Epoch : 21, Train Loss : 0.59, Train Accuracy : 0.81, Validation Loss : 1.42, Validation Accuracy : 0.52
Epoch : 22, Train Loss : 0.57, Train Accuracy : 0.82, Validation Loss : 0.89, Validation Accuracy : 0.71
Epoch : 23, Train Loss : 0.53, Train Accuracy : 0.83, Validation Loss : 0.86, Validation Accuracy : 0.72
Epoch : 24, Train Loss : 0.54, Train Accuracy : 0.83, Validation Loss : 1.08, Validation Accuracy : 0.66
Epoch : 25, Train Loss : 0.50, Train Accuracy : 0.84, Validation Loss : 0.81, Validation Accuracy : 0.72
Epoch : 26, Train Loss : 0.49, Train Accuracy : 0.84, Validation Loss : 1.35, Validation Accuracy : 0.58
Epoch : 27, Train Loss : 0.48, Train Accuracy : 0.85, Validation Loss : 0.93, Validation Accuracy : 0.70
Epoch : 28, Train Loss : 0.44, Train Accuracy : 0.86, Validation Loss : 1.13, Validation Accuracy : 0.67
Epoch : 29, Train Loss : 0.43, Train Accuracy : 0.87, Validation Loss : 0.74, Validation Accuracy : 0.76
Epoch : 30, Train Loss : 0.40, Train Accuracy : 0.88, Validation Loss : 0.96, Validation Accuracy : 0.68
Epoch : 31, Train Loss : 0.39, Train Accuracy : 0.87, Validation Loss : 0.78, Validation Accuracy : 0.75
Epoch : 32, Train Loss : 0.37, Train Accuracy : 0.88, Validation Loss : 0.85, Validation Accuracy : 0.74
Epoch : 33, Train Loss : 0.36, Train Accuracy : 0.89, Validation Loss : 0.78, Validation Accuracy : 0.75
Epoch : 34, Train Loss : 0.34, Train Accuracy : 0.89, Validation Loss : 1.03, Validation Accuracy : 0.68
Epoch : 35, Train Loss : 0.33, Train Accuracy : 0.90, Validation Loss : 1.20, Validation Accuracy : 0.68
Epoch : 36, Train Loss : 0.30, Train Accuracy : 0.91, Validation Loss : 0.61, Validation Accuracy : 0.80
Epoch : 37, Train Loss : 0.29, Train Accuracy : 0.91, Validation Loss : 1.66, Validation Accuracy : 0.64
Epoch : 38, Train Loss : 0.28, Train Accuracy : 0.91, Validation Loss : 0.56, Validation Accuracy : 0.82
Epoch : 39, Train Loss : 0.25, Train Accuracy : 0.93, Validation Loss : 0.77, Validation Accuracy : 0.76
Epoch : 40, Train Loss : 0.25, Train Accuracy : 0.92, Validation Loss : 2.17, Validation Accuracy : 0.58
Epoch : 41, Train Loss : 0.28, Train Accuracy : 0.92, Validation Loss : 1.03, Validation Accuracy : 0.69
Epoch : 42, Train Loss : 0.21, Train Accuracy : 0.94, Validation Loss : 0.57, Validation Accuracy : 0.83
Epoch : 43, Train Loss : 0.20, Train Accuracy : 0.94, Validation Loss : 0.55, Validation Accuracy : 0.83
Epoch : 44, Train Loss : 0.19, Train Accuracy : 0.94, Validation Loss : 0.54, Validation Accuracy : 0.83
Epoch : 45, Train Loss : 0.19, Train Accuracy : 0.94, Validation Loss : 2.19, Validation Accuracy : 0.61
Epoch : 46, Train Loss : 0.18, Train Accuracy : 0.95, Validation Loss : 0.51, Validation Accuracy : 0.84
Epoch : 47, Train Loss : 0.16, Train Accuracy : 0.96, Validation Loss : 0.89, Validation Accuracy : 0.75
Epoch : 48, Train Loss : 0.15, Train Accuracy : 0.96, Validation Loss : 0.52, Validation Accuracy : 0.84
Epoch : 49, Train Loss : 0.14, Train Accuracy : 0.97, Validation Loss : 0.61, Validation Accuracy : 0.81



















In [ ]:



    


fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,6))

fig.suptitle(f"model_nn_6_layers_dynamic : 50 Epochs, 0.001 SGD", fontsize=16)
ax1.plot(history_dynamic_6_layers['train_acc'], label='train accuracy')
ax1.plot(history_dynamic_6_layers['valid_acc'], label='valid accuracy')
ax1.legend()


ax2.plot(history_dynamic_6_layers['train_loss'], label='train loss')
ax2.plot(history_dynamic_6_layers['valid_loss'], label='valid loss')
ax2.legend()

fig.show()



    















    




























Different Model(Hidden Channels, Hidden Sizes & Optimizers)












In [ ]:



    


input = torch.randn(32,1,40,174)
input_channels = 1
cnn_hidden_channels = [16, 32, 64, 64]
hidden_sizes = [256]
output_size = 10
sample_image_batch_dim = (32,1,40,174)
model = DynamicModel(input_channels, cnn_hidden_channels, hidden_sizes, output_size, sample_image_batch_dim)
model = model.to(device)

history_dynamic_6_layers_2 = fit(model, train_dl, test_dl, 50, 0.001, optim=torch.optim.Adam)



    















    






Epoch : 0, Train Loss : 1.42, Train Accuracy : 0.50, Validation Loss : 1.03, Validation Accuracy : 0.65
Epoch : 1, Train Loss : 0.82, Train Accuracy : 0.72, Validation Loss : 0.83, Validation Accuracy : 0.73
Epoch : 2, Train Loss : 0.57, Train Accuracy : 0.81, Validation Loss : 0.65, Validation Accuracy : 0.77
Epoch : 3, Train Loss : 0.39, Train Accuracy : 0.87, Validation Loss : 0.59, Validation Accuracy : 0.80
Epoch : 4, Train Loss : 0.31, Train Accuracy : 0.90, Validation Loss : 0.59, Validation Accuracy : 0.81
Epoch : 5, Train Loss : 0.31, Train Accuracy : 0.90, Validation Loss : 0.74, Validation Accuracy : 0.78
Epoch : 6, Train Loss : 0.31, Train Accuracy : 0.90, Validation Loss : 0.49, Validation Accuracy : 0.85
Epoch : 7, Train Loss : 0.19, Train Accuracy : 0.93, Validation Loss : 0.54, Validation Accuracy : 0.85
Epoch : 8, Train Loss : 0.12, Train Accuracy : 0.96, Validation Loss : 0.54, Validation Accuracy : 0.85
Epoch : 9, Train Loss : 0.11, Train Accuracy : 0.96, Validation Loss : 0.61, Validation Accuracy : 0.85
Epoch : 10, Train Loss : 0.07, Train Accuracy : 0.98, Validation Loss : 0.72, Validation Accuracy : 0.83
Epoch : 11, Train Loss : 0.07, Train Accuracy : 0.98, Validation Loss : 0.65, Validation Accuracy : 0.86
Epoch : 12, Train Loss : 0.04, Train Accuracy : 0.99, Validation Loss : 0.73, Validation Accuracy : 0.86
Epoch : 13, Train Loss : 0.04, Train Accuracy : 0.99, Validation Loss : 0.80, Validation Accuracy : 0.85
Epoch : 14, Train Loss : 0.11, Train Accuracy : 0.97, Validation Loss : 0.84, Validation Accuracy : 0.82
Epoch : 15, Train Loss : 0.15, Train Accuracy : 0.96, Validation Loss : 0.66, Validation Accuracy : 0.85
Epoch : 16, Train Loss : 0.14, Train Accuracy : 0.95, Validation Loss : 0.70, Validation Accuracy : 0.84
Epoch : 17, Train Loss : 0.05, Train Accuracy : 0.98, Validation Loss : 0.71, Validation Accuracy : 0.85
Epoch : 18, Train Loss : 0.03, Train Accuracy : 0.99, Validation Loss : 0.72, Validation Accuracy : 0.86
Epoch : 19, Train Loss : 0.02, Train Accuracy : 0.99, Validation Loss : 0.80, Validation Accuracy : 0.85
Epoch : 20, Train Loss : 0.06, Train Accuracy : 0.98, Validation Loss : 0.90, Validation Accuracy : 0.84
Epoch : 21, Train Loss : 0.14, Train Accuracy : 0.96, Validation Loss : 0.81, Validation Accuracy : 0.83
Epoch : 22, Train Loss : 0.06, Train Accuracy : 0.98, Validation Loss : 0.81, Validation Accuracy : 0.85
Epoch : 23, Train Loss : 0.02, Train Accuracy : 0.99, Validation Loss : 0.70, Validation Accuracy : 0.88
Epoch : 24, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.69, Validation Accuracy : 0.88
Epoch : 25, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.76, Validation Accuracy : 0.87
Epoch : 26, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.72, Validation Accuracy : 0.88
Epoch : 27, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.74, Validation Accuracy : 0.88
Epoch : 28, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.74, Validation Accuracy : 0.87
Epoch : 29, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.73, Validation Accuracy : 0.87
Epoch : 30, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.76, Validation Accuracy : 0.87
Epoch : 31, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.77, Validation Accuracy : 0.88
Epoch : 32, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.77, Validation Accuracy : 0.88
Epoch : 33, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.77, Validation Accuracy : 0.88
Epoch : 34, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.78, Validation Accuracy : 0.88
Epoch : 35, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.78, Validation Accuracy : 0.88
Epoch : 36, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.79, Validation Accuracy : 0.88
Epoch : 37, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.81, Validation Accuracy : 0.88
Epoch : 38, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.80, Validation Accuracy : 0.88
Epoch : 39, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.83, Validation Accuracy : 0.87
Epoch : 40, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.82, Validation Accuracy : 0.88
Epoch : 41, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.83, Validation Accuracy : 0.87
Epoch : 42, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.85, Validation Accuracy : 0.88
Epoch : 43, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.83, Validation Accuracy : 0.88
Epoch : 44, Train Loss : 0.00, Train Accuracy : 1.00, Validation Loss : 0.84, Validation Accuracy : 0.87
Epoch : 45, Train Loss : 0.36, Train Accuracy : 0.90, Validation Loss : 1.11, Validation Accuracy : 0.72
Epoch : 46, Train Loss : 0.30, Train Accuracy : 0.91, Validation Loss : 0.93, Validation Accuracy : 0.79
Epoch : 47, Train Loss : 0.13, Train Accuracy : 0.96, Validation Loss : 0.89, Validation Accuracy : 0.82
Epoch : 48, Train Loss : 0.09, Train Accuracy : 0.98, Validation Loss : 0.90, Validation Accuracy : 0.83
Epoch : 49, Train Loss : 0.06, Train Accuracy : 0.98, Validation Loss : 0.91, Validation Accuracy : 0.84



















In [ ]:



    


fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,6))

fig.suptitle(f"model_nn_6_layers_dynamic : 50 Epochs, 0.001 SGD", fontsize=16)
ax1.plot(history_dynamic_6_layers_2['train_acc'], label='train accuracy')
ax1.plot(history_dynamic_6_layers_2['valid_acc'], label='valid accuracy')
ax1.legend()

ax2.plot(history_dynamic_6_layers_2['train_loss'], label='train loss')
ax2.plot(history_dynamic_6_layers_2['valid_loss'], label='valid loss')
ax2.legend()

fig.show()



    















    




























Future Scopes:
    

  • Just as we expected CNN to perform better on MFCC, maybe RNN can be perform better than them.
    • 

  • Experiment with multiple architectures to find some more insights into model behaviours.
    • 














In [ ]: